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Genetics of 35 biomarkers project in the Rivas Lab

Fig 1

Fig 2

We characterized the genetics of 35 biomarkers in UK Biobank. We performed the association and fine-mapping analysis to prioritize the causal variants, constructed the polygenic risk score (PRS) models, and evaluated their medical relevance with causal inference and PRS-PheWAS. We demonstrate a new approach, called multi-PRS, to improve PRS by combining PRSs across traits.

GWAS summary statistics

figshare data release documents

We uploaded the supplementary data on figshare.

Key info

Directory structure in this GitHub repository

  • covariate_correction: (for adjusting for statins and covariates)
  • snakemake: (for running the GWAS)
    • filtration: (for filtering GWAS results)
      • meta: (meta-analysis of the GWAS from multiple populations)
        • meta_flipfix: (flipping alleles on the meta-analysis)
        • cascade: (plotting of the variant effects)
        • phewas: (testing against other traits)
  • snpnet: (generation of polygenic risk scores)

File locations

  • Phenotype file location
    • Residual: @@@@@@/projects/biomarkers/covariate_corrected/outputExtendedNoTDIreduced/phenotypes/combined.20190810.phe
  • GWAS summary statistics
    • @@@@@@/projects/biomarkers_rivas/main/<population>/ukb24983_v2_hg19.<trait>.genotyped.glm.linear.gz
  • M-A
    • @@@@@@/projects/biomarkers_rivas/meta_flipfixed/METAANALYSIS_<trait>_1.tbl.gz

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